An attention mechanism incorporates positional information by applying a unique rotation to each query and key vector based on its absolute position in a sequence. The attention score between a query from position 't' and a key from position 's' is then computed. A key property of this rotation is that the dot product between the rotated query and key vectors is a function of the original vectors and the difference in their positions (t-s). Based on this information, what can be concluded about the attention scores produced by this mechanism?
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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An attention mechanism incorporates positional information by applying a unique rotation to each query and key vector based on its absolute position in a sequence. The attention score between a query from position 't' and a key from position 's' is then computed. A key property of this rotation is that the dot product between the rotated query and key vectors is a function of the original vectors and the difference in their positions (t-s). Based on this information, what can be concluded about the attention scores produced by this mechanism?
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